Analysis of bound substrates into active site

Dear All,

I am quite new in cryoEM SPA and I am working on a enzyme.
I can clearly see density for substrates in the active site and I wanted to know wheter there is variability in term of substrates lenght and positioning inside the active site.
How can I approach this problem ? I know I should generate some masks and refine but I am struggling at finding some examples in the literature.
My global resolution is around 3.2A and my structure has C2 symmetry (dimer).
How big should be the mask ? Should cover the whole asymmetric unit on just a region around the active site ?

With many thanks,

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Would any of these differences be expected to generate a concomitant conformational change in the protein?

Hi @giax ,
I’m pretty certain the answer is yes, there is variability of the substrate inside the active site, with various degrees depending on how specific/tight your enzyme binds its substrate.
You say that you see a good density for the substrate in the consensus map so that is a great start. Using 3DVA, you “can/may” see some variability in the substrate too, depending on your particle stack. Since you have a 3.2Å map, I recommend running 3DVA at 4 or 4.5Å to see global movements, and check each map for substrate density or not. I have a similar case where substrate is present in some maps but not all, in line with the physiological function of my protein.
If you then want to model substrate movement inside your protein, give phenix.varref a try. At your resolution, it is likely that your 3DVA maps will loose resolution for the substrate, you will have to apply restraints on ligand position.
Best of luck

The protein structure will definitely change from bound to unbound state as we have observed it already (we have a dataset for apo and another one with subtrates).
How can I mask part of my enzyme to highlight differencies in the active site ?
Also, should I try symmetry expansion (I have C2 dimer) ?
Can you point to some references in literature where people already tried that ?
Thanks !

Hi @giax! Welcome to the world of SPA! It’s great news that you’re already able to see density for your substrates. I have a few recommendations in addition to what others have already suggested:

If each of your asymmetric units can bind/unbind/catalyze reactions independently, I think your instinct to try symmetry expansion is spot on. This will let you treat each asymmetric unit of the enzyme separately which will likely improve your resolution if some particles have half bound and half unbound conformations.

Once you symmetry expand the particles, it’s important to only perform local refinements. This prevents accidentally creating duplicate particles in your particle stack!

I would try performing symmetry expansion and then making a mask around just one of the asymmetric units (whichever one looks better in your un-expanded maps) and performing a local refinement. This will give you your “consensus” map, where all of your asymmetric units are “stacked on top” of each other. In other words, you’ve lined everything up as best you can while ignoring the fact that some asymmetric units may be bound to substrate and others in the apo state.

From here, I would try performing a 3D classification with the same mask covering just one asymmetric unit. This job skips aligning the particles (since you’ve already done that by this point) and just focuses on classifying them between different maps. Since it doesn’t perform alignments, it can handle a far greater number of classes than something like heterogeneous refinement, which also needs to align the particles.

My first instinct is 3D classification over 3D variability analysis in this case since we’re looking for a binary switch — the enzyme is either in the apo state or the bound state. We know that there are likely continuous conformational changes between those two states, but most particles will be in one or the other. You can, of course, try both and see which works better!

Briefly, I want to mention that when you’re making a mask that cuts through protein density (like you will be here), it is very important that the mask has a soft edge. This prevents “ringing” artifacts which severely degrade the alignment quality. We recommend a minimum soft padding width of 5 * resolution / pixel size, all in angstroms. Since your map is currently going to about 3 angstroms, 15 / pixel size would be a good place to start, but I’d try a few different padding widths and seeing what works best. We have more advice on making masks in our guide!

I hope that’s helpful, and please come back with any questions you still have, or that come up as you run the jobs! I will look in the literature to see if I can find some good examples for you, but hopefully this is enough to get you started!